1. Field of the Invention
The present invention relates to a medical image-processing apparatus and a method for processing medical images, for processing image data of medical images. In particular, the present invention relates to a technology for aligning two medical images (image data thereof).
2. Description of the Related Art
Conventionally, a plurality of medical images of a subject taken at different times has been compared to ascertain the progress of a disease or state of treatment for examination of treatment policy (e.g., refer to Japanese Patent Laid-Open No. 2005-334219). In such a comparative interpretation of radiograms, it is necessary to align images that have been taken at different times in order to compare slice images (such as CT image) at the approximately same position as the subject. Along with the introduction in recent years of apparatuses that can obtain three-dimensional medical images, alignment of three-dimensional medical images has been necessary.
As for methods for aligning images, a repeated alignment method using similarities of images and an alignment method using landmarks (also known as “feature points”) are generally known.
The repeated alignment method using similarities of images has been disclosed, for example, in “Intensity-Based Image Registration Using Robust Correction Coefficients” by Jeongtae Kim, Jefferey A. Fessler, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23, No. 11, NOVEMBER 2004, pp. 1430-1444. This method includes repeated alignments using similarities of images such as correlation coefficients. This method is divided into a method called “rigid registration,” which is alignment without causing deformation of the image, and a method called “non-rigid registration,” which is alignment causing deformation of the image. In this specification, both of these methods are referred to as “registration.”
On the other hand, an alignment method using landmarks has been disclosed in “A Algorithm for Localizing Branching Points of Pulmonary Vessels for Non-Rigid Registration in Lung” by SHIKATA Hidenori et al., IEICE TRANSACTIONS, VOL. J85-D-II, No. 10, 2002, pp. 1613-1623), for example. The process time of this method is essentially shorter than the repeated alignment method that uses similarities of images. In other words, once the landmarks have been designated, a linear optimization method can be applied for the alignment of a pair of landmarks. Because repetition is unnecessary, it is possible to align images with a short process time of approximately 1 second or less, even when the data volume is large.
However, the following problems are involved in such conventional alignment methods described above.
First, in the repeated alignment method using similarities of images, a few dozens of local regions are set for each image to be compared, and similarities of images are calculated for each pair of corresponding local regions. At the same time, it repeatedly performs a process such as parallel shifting, rotation, scaling (size change), and deformation of images so that the degree of similarities of images becomes higher. This method is effective to some extent for two-dimensional images having small data volume, but for three-dimensional images having dramatically higher data volume, a long process time is required, so it can hardly be regarded as practical at the current stage.
For example, it is described in the aforementioned document “Intensity-Based Image Registration Using Robust Correction Coefficients” that it takes approximately 0.1 seconds to calculate image similarities of the local regions of two-dimensional images that are 10×10 pixels in size. If a similar process is applied to the local regions of three-dimensional images of that are 10×10×10 voxels in size, it is estimated to take simply 10×0.1=1 second. Therefore, when 50 pairs of local regions are set in a three-dimensional image, for example, it requires approximately 1×50=50 seconds per calculation.
Moreover, a publicly known optimization method is used for the algorithm of image alignment, and it has been known that the number of repetitions increases as the number of variables increases. For example, in non-rigid registration of three-dimensional images, it is necessary to optimize a few dozens of variables, which requires at least about 100 repetitions of calculation. Therefore, it is estimated to take approximately 50×100=5,000 seconds (approximately 83 minutes) for the alignment of three-dimensional images. Under such circumstances, it is expected to be difficult to achieve practical process time, even if a high-speed process method were to be added.
Meanwhile, in the alignment method using landmarks, there is a merit of shorter process time as described above, but there is difficulty in achieving accurate alignment. In other words, as described in the aforementioned document “A Algorithm for Localizing Branching Points of Pulmonary Vessels for Non-Rigid Registration in Lung,” when aligning an image containing pulmonary vessels, for example, in order to align precisely using landmarks, a highly precise determination of the position of branching points and an accurate graph representation of vessels are necessary. This is because an accuracy of specifying the corresponding landmarks is reflected in the accuracy of alignment for the landmark group extracted independently from each of the two images to be compared.
Moreover, in reality, it is extremely difficult to attain an accurate graph representation of an object with a complex network such as pulmonary vessels. This also has become a large obstacle in improving the accuracy of the alignment method using landmarks. In particular, because accuracy is regarded as very important in the medical field in order to avoid medical errors, it is believed that the alignment method using landmarks has low usability.